from __future__ import annotations

from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   str							, lowercase_				:   int | None = None)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    value
   _UpperCamelCase           =    random()
   _UpperCamelCase           =    None
   _UpperCamelCase           =    None







 def __repr__(		self				:   int)						->	str:


   """simple docstring"""







   from pprint import pformat

   if self.left is None and self.right is None:
     return f'\'{self.value}: {self.prior:.5}\''
   else:
     return pformat(
         {f'{self.value}: {self.prior:.5}': (self.left, self.right)}							, indent=1)







 def __str__(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    str(self.value) + " "
   _UpperCamelCase           =    str(self.left or "")
   _UpperCamelCase           =    str(self.right or "")
   return value + left + right


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->tuple[Node | None, Node | None]:




  '''simple docstring'''

  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      _UpperCamelCase      ,						_UpperCamelCase           =    split(root.left    ,     a__ )
      return left, root
    else:
      _UpperCamelCase      ,						_UpperCamelCase           =    split(root.right    ,     a__ )
      return root, right


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Node | None:




  '''simple docstring'''

  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _UpperCamelCase           =    merge(left.right    ,     a__ )
    return left
  else:
    _UpperCamelCase           =    merge(a__    ,     right.left )
    return right


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Node | None:




  '''simple docstring'''

  _UpperCamelCase           =    Node(a__ )
  _UpperCamelCase      ,						_UpperCamelCase           =    split(a__    ,     a__ )
  return merge(merge(a__    ,     a__ )    ,     a__ )


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Node | None:




  '''simple docstring'''

  _UpperCamelCase      ,						_UpperCamelCase           =    split(a__    ,     value - 1 )
  _UpperCamelCase      ,						_UpperCamelCase           =    split(a__    ,     a__ )
  return merge(a__    ,     a__ )


def 				lowerCAmelCase__       (			a__ )      ->None:




  '''simple docstring'''

  if not root:  # None
    return
  else:
    inorder(root.left )
    print(root.value    ,     end="," )
    inorder(root.right )


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Node | None:




  '''simple docstring'''

  for arg in args.split():
    if arg[0] == "+":
      _UpperCamelCase           =    insert(a__    ,     int(arg[1:] ) )

    elif arg[0] == "-":
      _UpperCamelCase           =    erase(a__    ,     int(arg[1:] ) )

    else:
      print("Unknown command" )

  return root


def 				lowerCAmelCase__       (			)      ->None:




  '''simple docstring'''

  _UpperCamelCase           =    None
  print(
      "enter numbers to create a tree, + value to add value into treap, "
      "- value to erase all nodes with value. 'q' to quit. " )

  _UpperCamelCase           =    input()
  while args != "q":
    _UpperCamelCase           =    interact_treap(a__    ,     a__ )
    print(a__ )
    _UpperCamelCase           =    input()

  print("good by!" )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




